Verification of SARS-CoV-2-Encoded small RNAs and contribution to Infection-Associated lung inflammation

  1. Zhang Cheng
  2. Liu Cheng
  3. Jiang Lin
  4. Cui Lunbiao
  5. Li Chunyu
  6. Song Guoxin
  7. Xu Rui
  8. Geng Xiangnan
  9. Luan Changxing
  10. Chen Yan
  11. Zhu Baoli
  12. Zhu Wei

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Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019 (COVID-19), the respiratory illness responsible for the COVID-19 pandemic. SARS-CoV-2 is a positive-stranded RNA virus belongs to Coronaviridae family. The viral genome of SARS-CoV-2 contains around 29.8 kilobase with a 5′-cap structure and 3′-poly-A tail, and shows 79.2% nucleotide identity with human SARS-CoV-1, which caused the 2002-2004 SARS outbreak. As the successor to SARS-CoV-1, SARS-CoV-2 now has circulated across the globe. There is a growing understanding of SARS-CoV-2 in virology, epidemiology, and clinical management strategies. In this study, we verified the existence of two 18-22 nt small viral RNAs (svRNAs) derived from the same precursor in human specimens infected with SARS-CoV-2, including nasopharyngeal swabs and formalin-fixed paraffin-embedded (FFPE) explanted lungs from lung transplantation of COVID-19 patients. We then simulated and confirmed the formation of these two SARS-CoV-2-Encoded small RNAs in human lung epithelial cells. And the potential pro-inflammatory effects of the splicing and maturation process of these two svRNAs in human lung epithelial cells were also explored. By screening cytokine storm genes and the characteristic expression profiling of COVID-19 in the explanted lung tissues and the svRNAs precursor transfected human lung epithelial cells, we found that the maturation of these two small viral RNAs contributed significantly to the infection associated lung inflammation, mainly via the activation of the CXCL8, CXCL11 and type I interferon signaling pathway. Taken together, we discovered two SARS-CoV-2-Encoded small RNAs and investigated the pro-inflammatory effects during their maturation in human lung epithelial cells, which might provide new insight into the pathogenesis and possible treatment options for COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2021.05.16.444324: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.3 Poly(A) RT-PCR combined with Pyrosequencing: The nucleic acid samples from pharyngeal swabs of 5 patients with COVID-19 were used for the screening and identification of the above six potential small viral RNAs (svRNAs) encoded by SARS-CoV-2, which were proposed by BLAST analysis.
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    The sequences of the control miRNA precursors were obtained from miRbase (http://www.mirbase.org).
    miRbase
    suggested: (miRBase, RRID:SCR_003152)
    2.7 Characteristic expression profiling analysis of COVID-19: Gene expression profile datasets were searched from Gene Expression Omnibus (GEO) database(http://www.ncbi.nlm.nih.gov/geo/) of National Centre for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/) with the keyword “SARS-CoV-2 OR COVID-19”.
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    https://www.ncbi.nlm.nih.gov/
    suggested: (GENSAT at NCBI - Gene Expression Nervous System Atlas, RRID:SCR_003923)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.05.16.444324v1 on bioRxiv